=Paper=
{{Paper
|id=Vol-52/paper-13
|storemode=property
|title=Ontology for Coordinating Dialogs in Distance Learning Environments
|pdfUrl=https://ceur-ws.org/Vol-52/oas01-eleuterio.pdf
|volume=Vol-52
}}
==Ontology for Coordinating Dialogs in Distance Learning Environments==
https://ceur-ws.org/Vol-52/oas01-eleuterio.pdf
Ontology for Coordinating Dialogs
in Distance Learning Environments
Marco A. Eleuterio Jean-Paul Barthès
PUC-PR Université de Technologie de Compiègne
Rua Imaculada Conceição, 1155 Rue Personne de Roberval
Curitiba – PR - Brasil BP 20529 - 60205 Compiègne Cedex
(55)-41-330-1555 (33) 03 44 20 48 13
marcoa@ppgia.pucpr.br barthes@utc.fr
ABSTRACT worksheets) or finishing the discussion. This process is
This paper discusses the role of ontology for an intelligent agent continued until a satisfactory degree of collective agreement is
designed for coordinating domain dialogs among participants of a achieved. The discussion is organized as an argumentation tree
distance learning environment. The domain models used by the [1], where each node corresponds to a dialog argument. Most of
agent are centered on the ontological representation of the the intelligent behavior featured by the system is the result of (i)
domain concepts. The agent’s behavior as well as the adequacy the regrouping algorithm that takes into account structural and
of the domain models have been tested in actual distance semantic parameters and (ii) the dynamic generation of
learning situations. discussion elements, both services being delivered by the KB
agent.
1. INTRODUCTION
Distance learning environments are widely used to allow full 3. THE KB AGENT AND THE ROLES OF
distance courses or used as a complement to more traditional THE ONTOLOGY
classes. Due to their inherent distributed nature, such The role of the KB agent is thus to perform all domain-specific
environments have a large potential for agent architecture, either tasks, i.e. generate discussion elements, perform semantic
for delivering learning material or enabling communication matching and evaluate the semantic coverage of the discussion.
among the participants in so-called discussion forums. In either The domain ontology, in our work, is the central representation
aspects, the ontological representation of the domain plays an that provides the KB agent with the required amount of theory-
important role. We are developing an agent system capable of awareness.
coordinating collective discussions in distance learning
environments by using a set of specialized agents. Two of the 3.1 Ontology for generating discussion
major agents of this system are the dialog agent and the elements
knowledge base (KB) agent. The generation of text-based questions in natural language (or
content-expected interrogative speech acts [2]) starts up a
The dialog agent coordinates the discussion by generating series discussion tree. We represent the domain by using two different
of dialog cycles and maintaining an argumentation tree. The KB models: the domain ontology and the task structure. The
agent performs all domain-related tasks, being the ontology its ontology relates domain concepts by means of part-of and is-a
central knowledge model. Considering the purposes of this links, and is used to represent the concepts manipulated by the
workshop, we will give more emphasis to the KB agent. tasks of the task structure. The ontology is used to produce
questions like: what kinds of do you identify, or what
2. THE DIALOG AGENT AND THE are the composing elements of ? Such questions are
ARGUMENTATION STRUCTURE produced so as to cover a certain number of concepts scheduled
In this section we briefly describe the dialog agent’s behavior. for the discussion.
This agent has the task of initiating, coordinating and closing the
discussions by generating dialog cycles. Initially, given a set of
3.2 Ontology for semantic matching
When a discussion element is generated, a central concept is
questions (discussion elements, or DEs) and a set of participants,
identified. It is the one appearing in the text. All related sub-
the dialog agent builds and distributes a set of questions to the
concepts that appear in the domain ontology are considered to be
participants. As soon as the questions are answered, the agent
the sub-domains of this discussion element. By analyzing the
reshuffles the groups of participants and sends the questions with
occurrence of such concepts in other answers or comments, the
their answers to be analyzed and commented by the new groups.
agent can discover semantic relations and use them for further
According to the level of agreement and the content of the
regrouping of the participants for the next discussion cycle.
comments, the system may decide upon triggering another
discussion cycle (regrouping the participants and building new
Workshop on Ontologies in Agent Systems (OAS2001) - Autonomous Agents 2001,
may 2001, Montreal, Canada.
4. OUR EXPERIENCE IN DOMAIN ontology by means of specialized links, namely output resource,
input resource and implicit knowledge resource, which specify
MODELING how a certain concept is used by the task (see Figure 1).
We have conducted experiences in domain modeling as part of a
research project between the Technology University of
Compiègne (UTC) and CEGOS, a French enterprise that 4.3 Implementation issues
provides on-line training. We chose a specific CEGOS course We implemented both the ontology and the task model in LISP,
and built the knowledge models for it, i.e., the task model and as two independent structures linked together by a set of
the domain ontology. We are now designing the agents’ specialized links.
mechanisms based on such models. The items below describe the
major results from this project concerning the KB agent.
We also implemented an editor that allows the domain experts of
CEGOS to build and edit their own ontologies (see Figure 2).
4.1 A “what-for” approach for designing the
ontology
Given the problem, we started with analyzing what the ontology
would be used for, and then we chose a representation for it. For
the purpose of generating discussion elements the ontology, as
well as the task model, should provide elements for building
interrogative sentences. Such sentences are meant to investigate
the domain along five different axes: (i) the nature of the
concepts (ontology is-a links); (ii) the elements of a composed
concept (ontology part-of links); (iii) the use of the concepts by a
certain task (task models resource link); (iv) the decomposition
of a complex task into sub-tasks (task model sequence link); and
(v) different ways of performing a task (task model type link).
The second use of the ontology is to perform semantic matching,
a process by which the agent dynamically regroups the
participants of the discussion according to the content of their
answers or comments. To this purpose, mainly is-a and part-of
links are used to measure the semantic distance between two text
Figure 2: Ontology and Task Model Editor
chunks.
4.2 Domain modeling
The course we modeled, named “Le responsable formation 5. CONCLUSION
nouveau dans sa fonction”, which can be roughly translated as In our work, we identified the need for an ontology and looked
“How to manage competence in an enterprise” covers several for an adequate representation for it. The complexity of an
different domains, ranging from human resource administration, ontology, however, is related to the type of use we intend for it.
to teaching methodologies and legal aspects. The diverse nature Our problem requires a terminological ontology, i.e., a
of the course content lead us to organize the needed ontology as a structured collection of terms. Other applications may need more
collection of domains. powerful ontologies, like those containing formal definitions
(interpretable ontologies), or even executable ontologies based
The strong “how-to-do” feature of the content lead us to make on the notion of task ontology and abstract code [3].
use of another model, the task structure, that represents the tasks
of a training a manager in her daily work. The next obvious 6. REFERENCES
choice was to link the task structure to the corresponding domain [1] Karacapilidis, N; Papadias D. A computational approach for
argumentative discourse in multi-agent decision making
Ontology environments. AI communications 11 (1998) 21-23.
Task model (a set of domains)
[2] Porayska-Pompa, K; Pain, H. Aspects of Speech Act
Output resource Categorisation: Towards Generating Teacher’s Language.
Input resource International Journal of Artificial Intelligence in Education
(2000).
Implicit knowledge
Is-a-link
Is-a-link
Seq-link Is-a-link
Part-of-link [3] Mizoguchi, R; Bourdeau, J. Using Ontological Engineering
Type-link Part-of-link
Part-of-link to Overcome Common AI-ED Problems. International
Journal of Artificial Intelligence in Education (2000).
Figure 1: Link between the task model and the
domain ontology